A dynamic choice model to estimate the user cost of crowding with large scale transit data
Author(s)
Bansal, Prateek
Horcher, Daniel
Graham, Daniel
Type
Journal Article
Abstract
Efficient mass transit provision should be responsive to the behaviour of passengers. Operators often conduct surveys to elicit passenger perspectives, but these can be expensive to administer and can suffer from hypothetical biases. With the advent of smart card and automated vehicle location data, operators have reliable sources of revealed preference (RP) data that can be utilized to estimate transit riders’ valuation of service attributes. To date, effective use of RP data has been limited due to
modelling complexities. We propose a dynamic choice model (DCM) for population-level longitudinal RP data to address prominent challenges. In the DCM, riders are assumed to follow different decision rules (compensatory and inertia/habit) and temporal switching between decision rules based on
experience-based learning is also formulated. We develop an expectation-maximization algorithm to estimate the DCM and apply our model to estimate passenger valuation of crowding. Using large-scale data of two months with over four million daily trips by an Asian metro, our DCM estimates show an increase of 47% in passenger’s valuation of travel time under extremely crowded conditions. Furthermore, the average passenger follows the compensatory rule on only 25.5% or fewer trips. These results are valuable for supply-side decisions of transit operators.
modelling complexities. We propose a dynamic choice model (DCM) for population-level longitudinal RP data to address prominent challenges. In the DCM, riders are assumed to follow different decision rules (compensatory and inertia/habit) and temporal switching between decision rules based on
experience-based learning is also formulated. We develop an expectation-maximization algorithm to estimate the DCM and apply our model to estimate passenger valuation of crowding. Using large-scale data of two months with over four million daily trips by an Asian metro, our DCM estimates show an increase of 47% in passenger’s valuation of travel time under extremely crowded conditions. Furthermore, the average passenger follows the compensatory rule on only 25.5% or fewer trips. These results are valuable for supply-side decisions of transit operators.
Date Acceptance
2021-12-14
Citation
Journal of the Royal Statistical Society Series A: Statistics in Society, 185 (2)
ISSN
0964-1998
Publisher
Royal Statistical Society
Journal / Book Title
Journal of the Royal Statistical Society Series A: Statistics in Society
Volume
185
Issue
2
License URL
Sponsor
The Leverhulme Trust
Identifier
https://rss.onlinelibrary.wiley.com/doi/10.1111/rssa.12804
Grant Number
ECF-2020-246
Subjects
Social Sciences
Science & Technology
Physical Sciences
Social Sciences, Mathematical Methods
Statistics & Probability
Mathematical Methods In Social Sciences
Mathematics
crowding valuation
dynamic preferences
expectation-maximization
inertia
smart card data
LABEL SWITCHING PROBLEM
LATENT MARKOV-MODELS
ROUTE CHOICE
VALUATION
STATE
Statistics & Probability
0104 Statistics
1403 Econometrics
1603 Demography
Publication Status
Published